suppressPackageStartupMessages({
library(data.table)
library(DESeq2)
library(gplots)
library(here)
library(hyperSpec)
library(parallel)
library(pander)
library(plotly)
library(tidyverse)
library(tximport)
library(vsn)
library(zinbwave)
library(Rtsne)
})
source(here("UPSCb-common/src/R/featureSelection.R"))
hpal <- colorRampPalette(c("blue","white","red"))(100)
samples <- read_csv(here("doc/samples_final.csv"))
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## ScilifeID = col_character(),
## SubmittedID = col_character(),
## Stages = col_character(),
## Description = col_character(),
## ID = col_character()
## )
filelist <- list.files(here("data/Salmon"),
recursive = TRUE,
pattern = "quant.sf",
full.names = TRUE)
Sanity check to ensure that the data is sorted according to the sample info
filelist <- filelist[match(samples$ScilifeID,sub("_sortmerna.*","",basename(dirname(filelist))))]
stopifnot(all(match(sub("_sortmerna.*","",basename(dirname(filelist))),
samples$ScilifeID) == 1:nrow(samples)))
name the file list vector
names(filelist) <- samples$ID
Read the expression at the gene level
counts <- suppressMessages(round(tximport(files = filelist,
type = "salmon",
txOut=TRUE)$counts))
combine technical replicates
samples$ID <- sub("_L00[1,2]", "",
samples$ScilifeID)
counts <- do.call(
cbind,
lapply(split.data.frame(t(counts),
samples$ID),
colSums))
csamples <- samples[,-1]
csamples <- csamples[match(colnames(counts),csamples$ID),]
read the expression for the pool of lincRNAs we found
linc_read <- read_delim("~/Git/lncRNAs/doc/time_expression_nc_filtered.tsv",
delim = " ")
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## Transcript.ID = col_character(),
## score = col_double(),
## S1 = col_double(),
## S2 = col_double(),
## S3 = col_double(),
## S4 = col_double(),
## S5 = col_double(),
## S6 = col_double(),
## S7 = col_double(),
## S8 = col_double(),
## maxn = col_double(),
## n = col_double(),
## peak = col_character()
## )
linc <- linc_read$Transcript.ID
counts <- counts[linc, ]
sel <- rowSums(counts) == 0
sprintf("%s%% percent (%s) of %s genes are not expressed",
round(sum(sel) * 100/ nrow(counts),digits=1),
sum(sel),
nrow(counts))
## [1] "0% percent (0) of 169731 genes are not expressed"
dat <- tibble(x=colnames(counts),y=colSums(counts)) %>%
bind_cols(csamples)
ggplot(dat,aes(x,y,fill=csamples$Stages)) + geom_col() +
scale_y_continuous(name="reads") +
theme(axis.text.x=element_text(angle=90,size=4),axis.title.x=element_blank())
i.e. the mean raw count of every gene across samples is calculated and displayed on a log10 scale.
The cumulative gene coverage is as expected, considering we have lincRNAs, caracterised by a really low signal.
ggplot(data.frame(value=log10(rowMeans(counts))),aes(x=value)) +
geom_density() + ggtitle("gene mean raw counts distribution") +
scale_x_continuous(name="mean raw counts (log10)")
The same is done for the individual samples colored by Stages.
dat <- as.data.frame(log10(counts)) %>% utils::stack() %>%
mutate(Stages=csamples$Stages[match(ind,csamples$ID)])
ggplot(dat,aes(x=values,group=ind,col=Stages)) +
geom_density() + ggtitle("sample raw counts distribution") +
scale_x_continuous(name="per gene raw counts (log10)")
## Warning: Removed 3230200 rows containing non-finite values (stat_density).
dir.create(here("data/analysis/salmon"),showWarnings=FALSE,recursive=TRUE)
write.csv(counts,file=here("data/analysis/salmon/raw-unormalised-gene-expression_data_linc.csv"))
For visualization, the data is submitted to a variance stabilization transformation using DESeq2. The dispersion is estimated independently of the sample tissue and replicate.
csamples$Stages <- factor(csamples$Stages)
there are a lot of zeros, so we use zinbwave
se <- SummarizedExperiment(assays=list(counts=as.matrix(counts)),
colData=as.data.frame(csamples))
zinb <- zinbwave(se,K=0,epsilon=1e12,
X="~Stages",
observationalWeights=TRUE)
save(zinb,file=here("data/analysis/salmon/zinb.rda"))
Check the size factors (i.e. the sequencing library size effect)
dds <- DESeqDataSet(zinb,design=~Stages)
## converting counts to integer mode
dds <- DESeq(dds,
sfType = "poscounts",
useT = TRUE,
minmu = 1e-6)
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## Warning in getAndCheckWeights(object, modelMatrix, weightThreshold = weightThreshold): for 12918 row(s), the weights as supplied won't allow parameter estimation, producing a
## degenerate design matrix. These rows have been flagged in mcols(dds)$weightsFail
## and treated as if the row contained all zeros (mcols(dds)$allZero set to TRUE).
## If you are blocking for donors/organisms, consider design = ~0+donor+condition.
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
save(dds,file=here("data/analysis/salmon/dds_linc.rda"))
vsd <- varianceStabilizingTransformation(dds, blind=TRUE)
vst <- assay(vsd)
vst <- vst - min(vst)
save(vst,file=here("data/analysis/DE/vst-blind_linc.rda"))
vsda <- varianceStabilizingTransformation(dds, blind=FALSE)
vsta <- assay(vsda)
vsta <- vsta - min(vsta)
save(vsta,file=here("data/analysis/DE/vst-aware_linc.rda"))
# prepare the data to build the network
#ID <- rownames(vsta)
#vsta <- cbind(ID,vsta)
#vsta_tibble <- as_tibble(vsta)
#write_tsv(vsta_tibble,path=here("data/analysis/DE/vst-aware_linc.tsv"))
#thing <- read_tsv(here("data/analysis/DE/vst-aware_linc.tsv"))
#library(Biostrings)
#seq <- readDNAStringSet("data/trinity/Trinity.fasta")
#names(seq) <- sub(" .*","",names(seq))
#IDs <- thing$ID
#writeXStringSet(seq[IDs],file="~/Git/lncRNAs/data/analysis/DE/linc_all.fasta")
meanSdPlot(log2(counts(dds)[!mcols(dds)$allZero,]+1))
meanSdPlot(log2(assay(zinb)+1))
meanSdPlot(vst[rowSums(vst)>0,])
meanSdPlot(vsta[rowSums(vsta)>0,])
pc <- prcomp(t(vsta))
percent <- round(summary(pc)$importance[2,]*100)
We define the number of variable of the model
nvar=1
And the number of possible combinations
nlevel=nlevels(dds$Stages)
We plot the percentage explained by the different components, the red line represent the number of variable in the model, the orange line the number of variable combinations.
ggplot(tibble(x=1:length(percent),y=cumsum(percent)),aes(x=x,y=y)) +
geom_line() + scale_y_continuous("variance explained (%)",limits=c(0,100)) +
scale_x_continuous("Principal component") +
geom_vline(xintercept=nvar,colour="red",linetype="dashed",size=0.5) +
geom_hline(yintercept=cumsum(percent)[nvar],colour="red",linetype="dashed",size=0.5) +
geom_vline(xintercept=nlevel,colour="orange",linetype="dashed",size=0.5) +
geom_hline(yintercept=cumsum(percent)[nlevel],colour="orange",linetype="dashed",size=0.5)
## Warning: Removed 2 row(s) containing missing values (geom_path).
pc.dat <- bind_cols(PC1=pc$x[,1],
PC2=pc$x[,2],
csamples)
p <- ggplot(pc.dat,aes(x=PC2,y=PC1,col=Stages,text=dds$ID)) +
geom_point(size=2) +
ggtitle("Principal Component Analysis",subtitle="variance stabilized counts")
ggplotly(p) %>%
layout(xaxis=list(title=paste("PC2 (",percent[2],"%)",sep="")),
yaxis=list(title=paste("PC1 (",percent[1],"%)",sep="")))
Filter for noise
conds <- factor(csamples$Stages)
sels <- rangeFeatureSelect(counts=vsta,
conditions=conds,
nrep=3)
vst.cutoff <- 1
mar <- par("mar")
par(mar=c(0.05,0.05,0.05,0.05))
hm <- heatmap.2(t(scale(t(vsta[sels[[vst.cutoff+1]],]))),
distfun=pearson.dist,
hclustfun=function(X){hclust(X,method="ward.D2")},
labRow = NA,trace = "none",
labCol = conds,
col=hpal)
plot(as.hclust(hm$colDendrogram),xlab="",sub="",labels=conds)
# The Biological QA is good, considering it's based on lincRNAs. We have no outliers.
# The sequencing depth decreased comparing to the previous analysis.
# Looking at the PCA, it could be interesting to do DE analysis to see in particular what's going on
# in S3 and S6. I consider those two stages relevant, because I think things are changes here.
#
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid parallel stats4 stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] Rtsne_0.15 zinbwave_1.12.0
## [3] SingleCellExperiment_1.12.0 vsn_3.58.0
## [5] tximport_1.18.0 forcats_0.5.1
## [7] stringr_1.4.0 dplyr_1.0.4
## [9] purrr_0.3.4 readr_1.4.0
## [11] tidyr_1.1.2 tibble_3.0.6
## [13] tidyverse_1.3.0 plotly_4.9.3
## [15] pander_0.6.3 hyperSpec_0.99-20201127
## [17] xml2_1.3.2 ggplot2_3.3.3
## [19] lattice_0.20-41 here_1.0.1
## [21] gplots_3.1.1 DESeq2_1.30.0
## [23] SummarizedExperiment_1.20.0 Biobase_2.50.0
## [25] MatrixGenerics_1.2.1 matrixStats_0.58.0
## [27] GenomicRanges_1.42.0 GenomeInfoDb_1.26.2
## [29] IRanges_2.24.1 S4Vectors_0.28.1
## [31] BiocGenerics_0.36.0 data.table_1.13.6
##
## loaded via a namespace (and not attached):
## [1] colorspace_2.0-0 ellipsis_0.3.1 rprojroot_2.0.2
## [4] XVector_0.30.0 fs_1.5.0 rstudioapi_0.13
## [7] hexbin_1.28.2 farver_2.0.3 affyio_1.60.0
## [10] bit64_4.0.5 AnnotationDbi_1.52.0 lubridate_1.7.9.2
## [13] splines_4.0.3 cachem_1.0.3 geneplotter_1.68.0
## [16] knitr_1.31 jsonlite_1.7.2 broom_0.7.4
## [19] annotate_1.68.0 dbplyr_2.1.0 png_0.1-7
## [22] BiocManager_1.30.10 compiler_4.0.3 httr_1.4.2
## [25] backports_1.2.1 assertthat_0.2.1 Matrix_1.3-2
## [28] fastmap_1.1.0 lazyeval_0.2.2 limma_3.46.0
## [31] cli_2.3.0 htmltools_0.5.1.1 tools_4.0.3
## [34] affy_1.68.0 gtable_0.3.0 glue_1.4.2
## [37] GenomeInfoDbData_1.2.4 Rcpp_1.0.6 softImpute_1.4
## [40] cellranger_1.1.0 vctrs_0.3.6 preprocessCore_1.52.1
## [43] crosstalk_1.1.1 xfun_0.21 testthat_3.0.1
## [46] rvest_0.3.6 lifecycle_0.2.0 gtools_3.8.2
## [49] XML_3.99-0.5 edgeR_3.32.1 zlibbioc_1.36.0
## [52] scales_1.1.1 hms_1.0.0 RColorBrewer_1.1-2
## [55] yaml_2.2.1 memoise_2.0.0 latticeExtra_0.6-29
## [58] stringi_1.5.3 RSQLite_2.2.3 highr_0.8
## [61] genefilter_1.72.1 caTools_1.18.1 BiocParallel_1.24.1
## [64] rlang_0.4.10 pkgconfig_2.0.3 bitops_1.0-6
## [67] evaluate_0.14 labeling_0.4.2 htmlwidgets_1.5.3
## [70] bit_4.0.4 tidyselect_1.1.0 magrittr_2.0.1
## [73] R6_2.5.0 generics_0.1.0 DelayedArray_0.16.1
## [76] DBI_1.1.1 pillar_1.4.7 haven_2.3.1
## [79] withr_2.4.1 survival_3.2-7 RCurl_1.98-1.2
## [82] modelr_0.1.8 crayon_1.4.1 KernSmooth_2.23-18
## [85] rmarkdown_2.6 jpeg_0.1-8.1 locfit_1.5-9.4
## [88] readxl_1.3.1 blob_1.2.1 reprex_1.0.0
## [91] digest_0.6.27 xtable_1.8-4 munsell_0.5.0
## [94] viridisLite_0.3.0